System and method for providing a score for a used vehicle

ABSTRACT

One embodiment of the system and method described herein provides a score generator system that generates an automated vehicle specific valuation of a used car based on the physical and historical attributes of that vehicle. This score may indicate the likelihood that the vehicle will be on the road in a specific period of time. The score may give an absolute percentage of such likelihood or it may give a value relative to all other used vehicles in a database, all other used vehicles of the same make/model/year, or a certain subset of the vehicles in a database. In one embodiment, the score generator system includes a data link module for linking vehicle data and filter module for applying a multi-level filters that process the linked vehicle data.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/893,609, filed Aug. 16, 2007, to be issued as U.S. Pat. No.8,005,759, which claims priority to U.S. Provisional Application No.60/838,468 filed Aug. 17, 2006; U.S. Provisional Application No.60/888,021 filed Feb. 2, 2007; and U.S. Provisional Application No.60/949,808 filed Jul. 13, 2007, each titled “System and Method forProviding a Score for a Used Vehicle;” the contents of all theseapplications are hereby incorporated by reference in their entireties.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosures, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND

1. Field of the Disclosure

This disclosure relates generally to automated vehicle analysis systemsfor grading and analyzing used vehicles.

2. Description of the Related Art

Buying a car can often be a difficult proposition. It is typically oneof the more expensive purchases that people make. There are manydifferent cars available, each having slightly different features, andprospective purchasers have numerous factors to take into account. Thecar buying process is often only exacerbated when purchasing a used car.There are few standards available to determine the quality of used carsand to determine whether the price is reasonable. It is therefore oftendifficult to accurately compare different used cars, both between two ormore cars having similar or identical make, model, and year as well asamong all used cars.

One information source that attempts to classify the relative values ofcars is the Kelley Blue Book®. This guide attempts to give approximatepricing values to used cars based on their make, model, year, and someother features. This can often be an imprecise guide, however, becausethe condition of the vehicle is often estimated and specific occurrencesin the life of a given vehicle are not taken into account.

Vehicle history reports can be used to determine more preciseinformation about a specific vehicle, but often these reports provide awealth of data without providing an overall picture of what that datameans. Comparisons of multiple vehicle history reports can be atime-consuming process—the wealth of data may necessitate time to do aline-by-line comparison and may be difficult to even judge just howdifferent line-items affect the quality or term of life of the differentcars.

SUMMARY

As such, there is a need for a system and method to help provide apotential buyer with a quick determination of how various used carscompare to each other. The present disclosure provides a system forautomated vehicle analysis and a method for providing a potential buyeror other user with, in an embodiment, a numerical vehicle score. In anembodiment, the vehicle score provides a relative ranking of a specificused car versus all used cars. In another embodiment, the vehicle scoreprovides a relative ranking of a specific car versus other used cars ofthe same make, model, and/or year, while yet another score may relate toa given class of vehicles (such as SUVs, luxury sedans, trucks, economycars, and the like). In another embodiment, the vehicle score providesan absolute score, rather than a relative one. This may correspond to aprobability that a car will be on the road in five, seven, ten years, orthe like. In another embodiment, the vehicle score is a determinant onthe vehicle valuation as published by guide companies such as KelleyBlue Book and NADA Used Car Guides. In yet another embodiment, thevehicle score can be calculated from the time the vehicle is first soldto the present day. For instance, a vehicle score for a five year oldvehicle can be calculated one time for each year of the vehicle's life,so that multiple scores steadily or rapidly decline based on thereported vehicle's history. Additionally, this same vehicle's score canalso be projected into the future, showing, for example, how a vehicle'sscore may further decline over five years. In an embodiment, this may bebased on the vehicle's current mileage, recent usage factors and thelike.

One aspect of the present disclosure provides a vehicle scoring method,including electronically receiving a vehicle identification, from auser; retrieving a set of vehicle records from at least one data source;linking vehicle records that correspond to a common vehicle; identifyinga set of vehicle factors from the linked vehicle records based on afirst set of filter criteria; providing weighted values for each factorin the set based on a second set of filter criteria; combining theweighted values into a vehicle score; and electronically providing thescore to the user system. Another aspect of the disclosure provides amethod of vehicle scoring that includes: accepting a vehicleidentification indicative of a vehicle; retrieving attributes associatedwith the vehicle; assigning values to the attributes relative to averagevalues for a universe of vehicles; weighting the assigned values; anddetermining an overall score. In one embodiment, the vehicle scoringmethod is specifically tailored to pre-owned vehicles and includesattributes relating to the vehicle's history.

Another aspect of the present disclosure provides a vehicle scoringsystem that includes: a computer system having a processor that supportsoperation of a software application; a data storage module that includesa number of vehicle data records and can communicate with the computersystem; a filter module including three filters—one for extractingrelevant vehicle-related data from the data storage module, a second forvaluing the relevant vehicle-related data, and a third for combining thevalues into a vehicle score; and an output module for reporting thevehicle score to a user. In an embodiment, the computer system iscapable of accepting a vehicle identifier and communicating theidentifier to the filter module for use in one or more of the filters.Yet another aspect of the present disclosure provides a system forgenerating a vehicle score. The system includes one or more databases ofvehicle information such as physical attributes and historical dataregarding specific vehicles. The system also includes a score generatingmodule capable of assigning values to vehicle attributes, weighting theassigned values, and combining the weighted values in an overall score.In one embodiment, the system evaluates each of a number of attributesof a specified vehicle against the same attributes of other vehicles andassigns a value to each attribute or set of attributes; typically thiswill be a numerical value. These values are weighted depending on whichfactors have more or less effect on a vehicle's life expectancy, futuremonetary value, or the like, and a final score is then determined bymerging the weighted factors. In one embodiment, the system alsoincludes a network interface module and/or is associated with a webserver, allowing a user to access the internet, browse to a web site,enter a vehicle identifier, and have the score displayed on a web site.

In an embodiment, a system in accordance with the disclosure gathers alarge amount of data from a number of different databases and datasources. This data can be linked to provide overall pictures ofindividual vehicle histories. Due to the large amount of data, in anembodiment, when determining a vehicle score, a first filter is appliedto restrict the data to that which is deemed relevant to the scoringprocess. A second layer filter can also be applied to translate therelevant data to numerical or other easily scored values. A third layerfilter can also be applied to provide weighted values, and a finalfilter can be applied to combine each weighted value into an overallscore. Different combinations and sets of filters may be used to providescores for individual vehicles (1) versus all others; (2) versus similarclasses, makes, and/or models; (3) versus similar model years; and thelike.

For purposes of summarizing this disclosure, certain aspects, advantagesand novel features of the disclosure have been described herein. Ofcourse, it is to be understood that not necessarily all such aspects,advantages or features will be embodied in any particular embodiment ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

A general architecture that implements the various features of thedisclosure will now be described with reference to the drawings. Thedrawings and the associated descriptions are provided to illustrateembodiments of the disclosure and not to limit its scope. Throughout thedrawings, reference numbers are reused to indicate correspondencebetween referenced elements.

FIG. 1 illustrates a block diagram of an embodiment of a system forgenerating a vehicle score.

FIG. 1B illustrates a block diagram detailing an embodiment of a scoregenerator in a system for generating a vehicle score.

FIG. 2 illustrates a flow diagram of a vehicle scoring method inaccordance with an embodiment of the present disclosure.

FIG. 3 illustrates a sample output box displaying the relative riskratings of factors that may go into a score in accordance with anembodiment of the present disclosure.

FIG. 4 illustrates a sample output box displaying scores in accordancewith an embodiment of the present disclosure.

FIG. 5 illustrates a sample of modeling data that may be used in anembodiment of the systems and methods of the present disclosure.

FIG. 6 illustrates a sample output box for display of a score to a userin accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In one embodiment, an automated vehicle analysis system receives datafrom one or more databases that store vehicle information, and applies amulti-layer set of filters to the data to generate a vehicle score for aused automobile. The vehicle score is preferably simple in itspresentation. In an embodiment, the multi-layered filters filter datausing various factors such as, for example, vehicle age, mileage,location of use, number of owners, and number and type of accidentsreported to determine a numerical value representative of the overallcharacteristics of a vehicle. In one embodiment, this score mayrepresent the likelihood that a car will still be on the road in fiveyears, for example. In one embodiment, a single vehicle may receive twosets of numerical scores—one set defining its position with respect toall makes and models, and the second defining its position with respectto same make/model/year vehicles.

In this way, for example, a 2002 Lexus ES having had several owners andhigh mileage may score well in general, but may be in the lower halfwhen compared to all other 2002 Lexus ES vehicles. Conversely, a 1993Nissan Sentra with relatively low mileage and one owner may score poorlyin general, but well against all other 1993 Nissan Sentras that arestill on the road.

The automated vehicle analysis system and methods go well beyondpresenting facts about a given vehicle and instead automaticallyinterpret the voluminous amounts of data to quickly deliver helpfuldecision information to a user in a generally easily understood format.

In an embodiment, the scores may represent a relative probability that aspecific car will remain on the road in five (5) years compared to allvehicles and those of the same make/model/year. In other embodiments,the score or scores may represent an actual percent probability that agiven car will be on the road in a specific number of months or years.

Various embodiments and examples of the systems and methods for scoringa vehicle will now be described with reference to the drawings. Likenumbers indicate corresponding parts among the drawings and the leadingdigit represents the figure in which such part was first shown.

Example Score

Before delving into the details of the system and method, it may beinstructive to set out an example of one embodiment. A prospectivepurchaser, or user, may be in the market for a used car. The user findsthree cars that are of interest and fall within the desired price range.One vehicle is a black 2002 Jeep Grand Cherokee; a second is a burgundy2003 Ford Explorer; and a third is a silver 2002 Jeep Grand Cherokee.Wishing to compare the three SUVs, the user may obtain the VehicleIdentification Number (VIN) for each vehicle and go to a websiteassociated with a system according to the present disclosure. When theuser enters each VIN, the number is transferred to an automated vehicleanalysis system.

This system retrieves vehicle specifications as well as reported historyitems from, in one embodiment, third party providers of suchinformation. As such, further details on the vehicle are identified. Forexample, the black Jeep, may have 40,231 miles on it, have been owned bytwo individuals, been registered in Chicago, may have been in onemoderately classified accident, have received all regular maintenance,and been reported stolen once. The Explorer on the other hand may have34,254 miles, been owned by one company for use as a rental car, beenregistered in Iowa, and been in three minor accidents. While thisinformation in and of itself may be helpful, it is often hard to comparethe two vehicles based on this information. For example, it may lookbetter that the Explorer has fewer miles, but it may be hard todetermine whether city or rural driving is more damaging. Similarly theeffects of individual usage versus rental usage or the fact of havingbeen reported as stolen versus having had three minor accidents can bedifficult to compare.

As such, a system as disclosed herein uses information related to alarge number of vehicles to create and apply multi-layer filters thatautomatically organize and manage incoming vehicle data to providevehicle scores. This process helps determine which factors are more orless important in determining whether a vehicle will be on the road in,for example, five years. In an embodiment, a filter that extracts thedata relevant to each of these factors generally comprises the firstlayer filter. This first layer can help reduce the processing resourcesrequired in further steps. For example, in an embodiment, informationsuch as auction records and whether or not a vehicle has been rentedcommercially may be filtered out as irrelevant. It is understood,however, that the same data may or may not be used or filtered out invarious embodiments.

In an embodiment, the factors may be translated into numerical valuesthrough a second filter layer. A third layer filter comprises a weightassigned to each factor based on the relative importance of each factorin the overall score. This third layer filter is applied to the datarepresentative of each individual vehicle, and the weighted values arecombined to generate the score. To further the example, the black Jeepmay receive a score of 85, the Explorer a 77, and the silver Jeep an 80(each with similar vehicle scoring between 84 and 78). The user, uponobtaining each of these values, then has a simple, standardized way ofcomparing each vehicle. The black Jeep has the best score of the three,and a user may decide to make that purchase, because it is most likelyto last the greatest amount of time. Additionally, however, the user mayuse these scores to haggle prices with the dealers. For example, thesilver and the black Jeeps seem relatively close in score, but the usermay be able to negotiate a better price on the Silver Jeep based on itsslightly lower score. In that case, it may be worth giving up a bit onthe score to gain the better price.

Also, in one embodiment, the system may give scores or relative valuesof some or all of the various factors, so that the user can get a senseof which factors had the most or least effects on the overall score.

System

Turning to FIG. 1, in an embodiment, a system for generating vehiclescores includes a score generator 102 and any of a number of vehicleinformation databases or data sources. These databases may include Titleand/or Registration Information database 104, Department of MotorVehicle Records 106, Auction Records 108, and/or Accident Records 110.Vehicle information may also be obtained or derived from dealer records,state agency records, independent agency records, and the like. Vehicleinformation may be gathered from one or more of these databases or datasources into a primary vehicle database or stored in separate databasesfor use by the score generator 102 as needed. A system may also includea modeler 114 that helps determine the weighting of various factors tobe used in a score. In an embodiment, the modeler 114 may include anactual or simulated neural network to help determine factor weightings.In an embodiment, modeler 114 may be a background process or module thatmay be run periodically regardless of specific score requests. Potentialmodeling processes are described in more detail below.

A more detailed view of an embodiment of a score generator 102 inaccordance with the teachings of this disclosure is pictured in FIG. 1B.Score generator 102 may preferably include one or more input/output(I/O) devices and interfaces 140 for communications with, for example,the databases 104, 106, 108, 110, a VIN decoder 112, and a modeler 114.Input from users and output from the score generator 102 may also becommunicated through the I/O devices 140. Typically a score generatorwill also include one or more memory units 142 and one or more centralprocessing units (CPUs) 144. A preferred embodiment of the scoregenerator 102 will also include a data link module 146 and a filtermodule 148. The data link module 146 advantageously provides linking ofassociated data from the various databases 104, 106, 108, and 110.Through the data link module 146, the databases, which may store data indiffering formats and/or according to different database keys, the scoregenerator 102 may create a more complete picture of individualautomobiles. In other embodiments, the data received from third partiesis parsed by the data link module 146 and stored in a database in alinked format. Similarly, there may be a large number of databasesstoring both useful and extraneous information; in an embodiment, thefilter module 148 may include a multi-layer filter or filter matrix. Oneor more layers can help reduce the amount of data being processed tothat which is useful. The filter module 148 thus helps provide moreefficient processing task for the CPU 144. The filter module 148 mayadditionally contain filter layers that translate and provide weightingto factors for scoring. In an embodiment, I/O devices 140, memory 142,CPU 144, the data link module 146, and the filter module 148 may beconnected through a bus, network, or other connection. In an embodiment,the modeler 114 comprises a filter generator for filter module 148.

In an embodiment, an I/O device 140 of a score generator 102 accepts aninput—such as a VIN—representing the vehicle to be scored. Thisinformation may be stored temporarily in memory 142 or used to retrievevehicle attribute and history information from the various databases—orretrieve one file or complete database entry in the case of a combinedprimary vehicle database, or retrieve information from a combination ofa combined primary vehicle database and one or more of the others. A VINmay also be passed to a VIN decoder 112 through an I/O device 140 toretrieve vehicle attribute information, such as make, model, year,country of manufacture, engine type, and the like. This is possiblebecause VINs are unique identifiers with such information coded intothem according to a standard that is agreed upon by car manufacturers.

In an embodiment, the score generator 102 may then select attributesthat will be factors in the scoring. In an embodiment, the data linkmodule 146 accepts records from the various databases 104, 106, 108, 110and/or outside sources and links together records that correspond to theinput VIN. Some records may not have an associated VIN, in which casethe data link module 146 can utilize some other common element or set ofelements to link the records. For example, many records that do notinclude a VIN include a license plate number which can be used inconjunction with a record, such as a DMV report to link the VIN. Aslicense plates may be transferred among cars, the dates of the disparaterecords may help dissociate ambiguities. The filter module 148, in anembodiment, can filter the incoming records from the databases to limitextraneous data that is linked by the data link module 146. In analternative embodiment, the data link module 146 may link all incomingdata associated with a VIN, and then pass the linked information to thefilter module 148 to filter out extraneous data. Additional filterlayers for filter module 148 may convert the target vehicle's attributesinto numerical values for those factors. This conversion for each factorvalue may be based in whole or in part on a comparison with an averagefactor value for the vehicles in the comparison pool (such as allvehicles, a specific vehicle class, or a specific make/model/year). Yetanother filter layer may then multiply each of these factor values by aweight for the given factor; the results are summed to create arepresentative score. The weighting filter layer gives each factoredattribute a larger or smaller impact on the final score based on factorsthat are more or less important. A representative example will bediscussed below to help give a more concrete representation of theseconcepts.

Once a vehicle score is determined it may be output to the requestinguser via a display screen, a printer, output audibly, and the like. Inan embodiment, the score generator 102 may be connected to or part of aweb server that accepts queries from users via the internet, such as bya web browser. For example, a user may utilize an internet browser toaccess a website, such as Autocheck.com (http://www.autocheck.com) orother website currently operated by the assignee of the presentdisclosure. The user may enter the VIN number of a 2002 Audi A4 that heor she is considering purchasing; this VIN number is relayed to thescore generator 102. In other embodiments, the use may enter a VIN on athird party website, which will be passed to the score generator 102.Score generator 102 retrieves information about that car. In doing so,it may independently verify the make, model, and year of the car. It mayalso retrieve the number of registered owners based on DMV or otherrecords; the number and severity of accidents reported based on policereports, insurance company claims, or some other source; the locationsregistered; and the like. These factors may be selected and givenindividual values. For example, if no accidents were reported, the carmay receive a ten (10), a car with one minor accident a seven (7), a carthat was in several major accidents a two (2), etc. Each of the factorsis then weighted. For example, the accident value may be relativelyimportant and be weighted at six-tenths (0.6), while the location usedmay be less important and receive only a weighting of two-tenths (0.2).All of these resulting values may then be added to receive a finalscore, such as that the car ranks a 7.8 against all cars. A differentpass through the score generator 102, may show that the car only ranks a4.6 against all other 2002 Audi A4s, however. In an embodiment, this mayindicate that the specific car is well more likely than the average carto still be on the road in five years, but that it is somewhat lesslikely than the average 2002 A4 to be on the road in five years(assuming an average value of five (5) for each).

In an embodiment, the score generator 102 may output the final score (orscores) to the user as a portion of a web page operated by the operatorof the present system. In other embodiments, the final score or scoresmay be sent to a third party web server for display on a web page of athird party.

System Information

The various features and functions described in this document may beembodied in software modules executed by one or more general purposecomputing devices or components, such as the CPU 144. The modules may bestored in any type of computer readable storage medium or device.

Suitable hardware for a vehicle scoring system includes a conventionalgeneral purpose single-chip or multi-chip microprocessor such as aPentium® processor, a Pentium® II processor, a Pentium® Pro processor,an xx86 processor, an 8051 processor, a MIPS® processor, a Power PC®processor, or an ALPHA® processor. In addition, the microprocessor maybe any conventional special purpose microprocessor such as a digitalsignal processor. Furthermore, the score generator 102 may be used inconnection with various operating systems such as: Microsoft® Windows®3.x, Microsoft® Windows 95, Microsoft® Windows 98, Microsoft® WindowsNT, Microsoft® Windows XP, Microsoft® Windows CE, Palm Pilot OS, OS/2,Apple® MacOS®, Apple® OS X®, Disk Operating System (DOS), UNIX, Linux®,VxWorks, or IBM® OS/2®, Sun OS, Solaris OS, IRIX OS operating systems,and so forth. In an embodiment, an I/O device and interface 140 may be anetwork device and a network interface module to facilitatecommunication between it and user access points. The VIN decoder 112,databases 104, 106, 108, 110, and/or the modeler 114 may be implementedon the same or disparate hardware as the score generator 102. Forexample, in an embodiment, one or more of the modeler, 114, databases104, 106, 108, 110, and/or VIN decoder 112 are part of the scoregenerator 102.

User Access

As stated, user access may be through a web-enabled user access pointsuch as the user's personal computer or other device capable ofconnecting to the Internet. Such a device will likely have a browsermodule that may be implemented as a module that uses text, graphics,audio, video, and other media to present data and to allow interactionwith data via the communications network. The browser module may beimplemented as a combination of an all points addressable display suchas a cathode-ray tube (CRT), a liquid crystal display (LCD), a plasmadisplay, or other types and/or combinations of displays. In addition,the browser module may be implemented to communicate with input devicesand may also include software with the appropriate interfaces whichallow a user to access data through the use of stylized screen elementssuch as, for example, menus, windows, dialog boxes, toolbars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the browser module may communicate with a set ofinput and output devices to receive signals from the user. The inputdevice(s) may include a keyboard, roller ball, pen and stylus, mouse,trackball, voice recognition system, or pre-designated switches orbuttons. The output device(s) may include a speaker, a display screen, aprinter, or a voice synthesizer. In addition a touch screen may act as ahybrid input/output device. In another embodiment, a user may interactwith the system more directly such as through a system terminalconnected to the score generator without communications over theInternet, a WAN, or LAN, or similar network.

In one embodiment, a user access point comprises a personal computer, alaptop computer, a Blackberry® device, a portable computing device, aserver, a computer workstation, a local area network of individualcomputers, an interactive kiosk, a personal digital assistant, aninteractive wireless communications device, a handheld computer, anembedded computing device, or the like.

Sample Scoring Process

Although a process that score generator 102 may go through in anembodiment of the present disclosure was discussed briefly above,another process embodiment and some alternatives will be discussed withreference to FIG. 2. Beginning with a vehicle identifier, such as a VIN,or a set of identifiers, data on the target vehicle is gathered orretrieved in block 220; the data link module 146 may help link thedisparate records that are gathered. This may be from a precompileddatabase or one or more of the sources discussed above. This informationpreferably at least includes information relating to each factor goinginto the scoring. If there is missing or unreported information, anegative factor value may be presumed or the factor may be disregarded.Alternatively, an average value may be presumed. If any factors do notnecessarily reflect complete data, this can be reported to the user inorder to provide the user a gauge of the score's potential error.

In block 222, data gathered on the target vehicle is compared to theother vehicles in the database. Target attributes may be compared toactual vehicle data or a precompiled amalgamation of the vehicles of agiven class. For example, the average number of owners, the averagemileage, and other average values may be predetermined and stored foreasy comparison. This precompilation of data may be preferable to reduceprocessing resources required for each score request. Preferably, in anembodiment, these hypothetical average cars are created for each classfor which scoring is available. For example, in an embodiment, thesystem may store the attributes of a hypothetical average overall car,as well as hypothetical average make/model combinations.

Based in part on these comparisons, various factors going into thevehicle score are translated into numerical values for the target car inblock 224; this may be accomplished through one or more layers of afilter module 148. In some embodiments, these individual factor valuesmay be reported to the user, in numerical or graphical form. A sampleoutput of such data is shown in FIG. 3. Each of the factors listedtherein are possible factors in one or more embodiments, and more willbe discussed below. In FIG. 3, the individual factors are represented asthe relative risk to a potential purchaser of problems with the vehicle.Each factor could also be individually reported as a percentagerepresentation of the vehicle's likelihood of being on the road in five(5) years based on that factor, or otherwise in line with the overallscores.

Returning to FIG. 2, each of the assigned values for the given factorsare multiplied by a factor weight through, in an embodiment, a filterlayer of module 148 (block 226). This gives the more important factors,as determined by prior modeling, a greater impact on the overall scorethan less important factors. For example, in an embodiment, if car coloris a factor but is determined to have no effect on the likelihood of avehicle being on the road in five years, the color factor weight wouldbe zero (0) so that it does not impact the results. In an embodiment,mileage may have significant effect on the overall score and thus get arelatively high value, such as eight-tenths (0.8). Block 228 sums theresulting weighted factor values to achieve a final vehicle score.Again, a filter layer may be employed to sum or otherwise combine thevarious factors. Sample resulting scores for an overall vehicle scoreand a make/model vehicle score are shown in FIGS. 4 and 6. In FIG. 4,the numerical scores are given as well as a relative risk bar graph. Thegraphic view may be preferred for quick review, particularly of a singlecar, and seeing whether or not it is at risk of lasting less than agiven number of years and so on. The numerical score, on the other hand,may provide a more accurate way to compare multiple cars that apurchaser is considering.

It is, of course, understood that this is just one method of arriving ata score. The final weighted factor values may be averaged or otherwisecombined in other embodiments. In some embodiments, the final weightedfactor values may also be used in some of the created filters and not inothers. The scale for the score will be well known to be variable bythose of skill in the art. One embodiment may produce scores between 0and 100, another 0 to 10, yet another between 0% and 100%, and so on.Conveying the range of the scale to a user should provide sufficientcontext for a user to interpret the vehicle scores that are reported.For example, range information may include absolute highest and lowestactual and/or theoretical scores for all vehicles for which anembodiment of the disclosure has information. In an embodiment, a usermay be presented a range of some or a majority of the scores of anyautomobile in the database. One range may include the median 50% of carscores, for example.

Looking to FIG. 6, there is a sample “Summary” output box, such as maybe included in a website output to a user. As shown, the “Summary” boxmay include general information on the specific vehicle, such as itsVIN, make, model, class, age, and the like. FIG. 6 also shows an insetbox with a vehicle score 630 of “89.” A score range 632 for similarvehicles is also shown as “87-91.” As stated, this range may indicatethat the median 50% of similar vehicles will fall within this scorerange 632. As such, 25% of vehicles would score below and 25% wouldscore above this range 632. In the example shown, the Nissan 350Z whoseVIN was input scores right at the median. It is understood that therange may differ among embodiments. For example, different medianpercentages, standard deviation calculations, and/or the like maydetermine the range. The sample shown in FIG. 6 also indicates that“similar vehicles” are generally within a specific range. The definitionof similar vehicles may change among various embodiments. For example,age ranges and/or class designations may define the population of“similar vehicles.” In another embodiment, for example, similar vehiclesmay be limited to the same or a set of makes, models, model years,“style/body” designations, and/or the like.

Score Reporting Options

As alluded to above, there are a number of options for presenting avehicle score to a user, as well as the information, if any, whichprovides additional context to the score. An embodiment of the disclosedsystem may comprise or be in communication with a web server. In such anembodiment, a user may access scores by entering a VIN on a website andreceiving output such as shown in one or both of FIGS. 4 and 6. A usedcar listing on the web, such as one provided by a dealer, a classifiedssite, or the like, may also provide a link to access a vehicle score ofa stored VIN, in addition to or instead of requiring user entry of aVIN.

In various embodiments, the vehicle score may be provided directly to auser through the system or to a user through a third party web site. Inan embodiment including a third party web site, there are variousoptions for reporting the score. In one embodiment, the system mayoutput the score in any of a number of formats, such as xml format, forinterpretation and inclusion in a web page controlled by the thirdparty. For example, looking to FIG. 6, the third party may control thelayout and information included by retrieving the vehicle score 630 fromthe score generator 102 (FIG. 1) and including it in its own web pagelayout. In another embodiment, a third party may cede control of aportion of the web page, such as the inset score box or tile 634, to anembodiment of the disclosed system. In such a case, for example, thethird party web page may include code, such as an applet, that directsthe requesting user system to a web server associated with the disclosedsystem or the score generator 102 itself to complete the score tile 634.The latter embodiment may be preferable as it can provide additionalsecurity and reliability to the score, because it may be more difficultfor the third party web site to tamper with the score.

In either case, it is preferred that the look-up and scoring be dynamic,meaning that each time the vehicle score tile 634 is loaded, the scoringof the vehicle is redone by the score generator 102. This helps toensure that a user is viewing the most accurate score available, basedon the most recent data updates regarding the vehicle history of the carfor which he or she is seeking a score. Dynamic scoring can also allowincreased design flexibility. For example, in an embodiment, users maybe able to customize scores based on one or more factors that theyconsider most important. For example, a user may wish to skew higherscoring toward vehicles driven mostly in rural locations as opposed tourban environments, considering that more important. Additionally, in anembodiment, dynamic scoring allows scoring models to be updated withoutrequiring huge amounts of processing time to rescore all vehicles.Although less preferred, scores may also be retrieved from databasesthat store the scores calculated on some periodic or random basis, suchas daily, weekly, or the like.

Factors

The factors generally will relate to the physical characteristics of thevehicle and/or the history of the vehicle. Any of a number of suchattributes may be used in certain embodiments, but factors are likely tobe one or more of the following: make, model, year, engine type,equipped options, number of owners, mileage, number of accidents,severity of accidents, length of time retained by each owner,location(s) of primary usage, length of any remaining warranties,maintenance history, type of use (e.g. fleet vehicle, government, rentalvehicle, taxi, privately owned, etc.), number of times at auction,emissions test records, major branding events (such as a lemonnotification, manufacture buyback, total loss/salvage event, water/flooddamage, or negative announcement at auction), odometer branding,odometer rollback modeling, stolen vehicle records, repossessionrecords, and the like. Other factors may include driver education data,whether or not a vehicle was used as crash test vehicles, vehicle safetyinformation, crash test ratings, recall information, and the like. Otherembodiments may have additional factors not mentioned here, and factorslisted here may not be included in all embodiments.

In an embodiment, some of the factors may be numerical values used inraw form, such as the actual number of owners of a car or the absolutenumber of accidents in which a car was involved. In an embodiment, someof the factors may be relative numbers, such as a value between one (1)and ten (10), with, for example, ten (10) representing far less mileagethan the average vehicle of a given age and one (1) representing farmore mileage than an average vehicle of the given age. It should berecognized that some factors may be either actual or relational invarious embodiments, such as mileage or the length of time specificowners held the car.

Additionally, some factors may be derived values that are based onnon-numeric attributes, amalgamations of various individual attributes,related to different numerical attributes, and the like. For example, avalue may be derived based on the relative longevity or brand desire ofspecific makes (a BMW may get a higher value than a Kia, for example).In an embodiment, individual attributes may be analyzed together tocreate a single factor value such as for maintenance, which may includeboth the costs and number of maintenance events. A car's location, basedon zip code or other identifier, may be ranked with a high, medium, orlow risk value such as for flood prone areas versus high salt areasversus temperate areas, or rural versus urban areas, and the like.

While a number of the possible factors have been enumerated herein, itis understood that not all such factors may be utilized in any givenembodiment. It would also be known to one of skill in the art thatothers not mentioned may be utilized in a similar manner or toapproximate some of those factors mentioned herein. The presentdisclosure is not limited by the specific factors but is defined by thelimitations of the claims.

Additionally, one or a subset of factors may be determined to have amore significant effect on a resulting score or affect which otherfactors should be included for more accurate scoring. In such a case,multiple models may be used for different subsets of the overall vehiclemarket. For example, it may be determined that the age of the vehicle isa very significant factor in determining its score. Once age is brokenout, the number of accidents may be the most significant factor indetermining a score of a relatively new car, whereas a much older carmay be affected mainly by the brand or quality of production of theolder car or the number of owners it has had. It is understood from thedisclosure herein then that a score generator 102, in an embodiment, mayutilize different “sub-models” to generate scores for different segmentsof the market or to contribute to the overall model. Such an embodiment,where vehicles in different age categories have different factors andweightings that contribute to each score, is described in more detailbelow.

Modeling

In order to be able to determine which factors to include and/or whichincluded factors should be weighted most heavily in determining thevehicle scores (such as to create various filter layers for the filtermodule 146), it may be useful to model the weights of one or more setsof factors to determine the relative correlations of each factor to theultimate outcome. There are a number of modeling techniques that may beused to determine these weights.

It is generally helpful to gather a study file, in this case a largenumber of vehicle records, including information on all of the potentialfactors that are believed might be useful in predicting the scores. Tocontinue the example of a score representing the likelihood of a vehiclestill being on the road in five years, it is necessary to include datafrom both vehicles that remained on the road during a given time periodand vehicles that did not. FIG. 5 gives a diagrammatic representation ofthis. In order to model the likelihood of cars being on the road afterfive years, a time cutoff Y must be chosen that is at least five yearsprior to the last data available. The only information relevant afterthis time period then, in this example, is a binary value of whether ornot the vehicle is still on the road. In FIG. 5, Vehicles 1, 3, 6, and 8were still on the road, and the others had been salvaged, junked, orotherwise reported to be off the road (this may also come from anassumption, such as that the car's registration had lapsed for a certainperiod of time, such as more than 18 months, based on state registrationrules). All the data on the vehicles, prior to the time Y is thenpotential factor data. Data that may be gathered for this study fileincludes: the Vehicle In or out of Operation designation; number ofowners; odometer reading prior to Y; mileage by owner(s); VINDetail—make, model, year, engine size, country of manufacture,performance options, number off the line, etc.; brands prior to Y,meaning adverse events registered by a state (such as lemondesignations, manufacturer buybacks, salvage yard notices, negativeannouncements at auction); Geography—MSA (metropolitan statisticsarea)/state/zip/latitude/longitude/etc. by owner; number of monthsretained by owner(s); number of accidents reported before Y; number oftimes at auctions prior to Y; any indication of odometer rollback (thismay be a calculated value); MSRP Value at Y, at time of retail, and/orat other specific other times; number of times failed emissions;purchase type by owner (such as whether use would be forfleet/government/lease/individual ownership/etc.).

Initial weights for each factor may be assigned at random or mayrepresent estimations. Changing the weight of the various factors maythen result in better or worse models. Such modeling may be done by anumber of well-known methods such as through the use of neural networks,logistic regression and the like. The approach may also be hands-on withstatisticians or others aiding the modeling process or automated, suchas with back propagation in a neural network to improve modeling.

Details of an Embodiment

The following is a description of an embodiment of a vehicle scoringmodel, according to the present disclosure, including codingspecifications. As can be seen, this embodiment utilizes multiple modelsfor different vehicle age categories. In this embodiment, the modelswere built to estimate the likelihood that a vehicle will be on the roadin 5 years. The probability created by these models is the score forthis embodiment.

The following outlines a detailed procedure for implementing oneembodiment of the present disclosure, titled AutoCheck Vehicle score.This model should be applied to each vehicle based upon the age of thevehicle and only those vehicles with event history information. Separatemodels were developed for six mutually exclusive age groups, namely 0-3years; 4-5 years; 6-8 years; 9-10 years; 11-12 years; and 13+ years. Thefollowing disclosure includes details of an example process forobtaining a score, based on the testing of a large sample set of data byExperian, the assignees of the present disclosure. It is important torecognize that this section is describing one embodiment only, and itwill be easily understood from the teachings herein how other factors,weighting, combinations thereof, and the like can be used in a myriad ofways to create other embodiments in keeping with these teachings.

Utilizing standard modeling techniques as discussed above, it wasdetermined that a number of input variables were of value. Variablestaken from the database(s) records may be identified as “primaryfactors.” In this embodiment, they include the make and model year ofthe vehicle. The manufacturers suggested retail price (“MSRP”) of thevehicle and the value of loan are also utilized; this information may beobtained from publicly available sources such as Black Book, availablefrom National Auto Research. In this embodiment, a vehicle class asdesignated by Automotive News, a well-known automotive industrypublication, is a factor. Whether or not a vehicle is AutoCheck®Assured® is another factor. AutoCheck® Assurance is a service publiclyavailable from Experian, the assignees of the present disclosure, athttp://www.autocheck.com. It takes into account factors such as titlebranding, theft, water damage, auction branding, and the like. The totalnumber of owners is another factor, and for each owner, state and ziplocation factors are utilized. Additionally, odometer readingsthroughout the life of the vehicle and various events in the history ofa car, as well as the timing of each event, are recorded as factors. Itis to be understood that any number of events may be recorded andutilized, and preferably all recorded events in a vehicle's history arefactored into the score. Events in this example include emissionschecks, use of the vehicle by the government, specific use by police,accident, theft, and repossession occurrences, and whether the vehiclewas used as a taxi or limousine. Similarly data on each of multipleowners may be used. The input variables are listed in Table 1 andspecific event variable codes are listed in Table 2.

TABLE 1 INPUT VARIABLES Variable Description VEHICLE DATA MODELYR ModelYear of Vehicle VALMSRP Value of MSRP VALLOAN Value of Loan MAKETXT MakeText of Vehicle VEHCLASS Vehicle Class HASSURED AutoCheck AssuredTOTOWN1 Total Number of Owners OWNER #1 STATE1 State of Current OwnerZIPLOC1 Zip Locality of Current Owner OWNER FILE LEASE01 Lease Flag -Owner #1 LEASE02 Lease Flag - Owner #2 LEASE03 Lease Flag - Owner #3LEASE04 Lease Flag - Owner #4 LEASE05 Lease Flag - Owner #5 LEASE06Lease Flag - Owner #6 LEASE07 Lease Flag - Owner #7 LEASE08 Lease Flag -Owner #8 LEASE09 Lease Flag - Owner #9 LEASE10 Lease Flag - Owner #10EVENT HISTORY DATA EODO1 Odometer Reading - Event #1 EODO2 OdometerReading - Event #2 EODO3 Odometer Reading - Event #3 EODO4 OdometerReading - Event #4 EODO5 Odometer Reading - Event #5 EODO6 OdometerReading - Event #6 EODO7 Odometer Reading - Event #7 EODO8 OdometerReading - Event #8 EODO9 Odometer Reading - Event #9 EODO10 OdometerReading - Event #10 EDATYR1 Event Year - Event #1 EDATYR2 Event Year -Event #2 EDATYR3 Event Year - Event #3 EDATYR4 Event Year - Event #4EDATYR5 Event Year - Event #5 EDATYR6 Event Year - Event #6 EDATYR7Event Year - Event #7 EDATYR8 Event Year - Event #8 EDATYR9 Event Year -Event #9 EDATYR10 Event Year - Event #10 EDATMT1 Event Month - Event #1EDATMT2 Event Month - Event #2 EDATMT3 Event Month - Event #3 EDATMT4Event Month - Event #4 EDATMT5 Event Month - Event #5 EDATMT6 EventMonth - Event #6 EDATMT7 Event Month - Event #7 EDATMT8 Event Month -Event #8 EDATMT9 Event Month - Event #9 EDATMT10 Event Month - Event #10ECHEK01 Event Checklist - Event #1 (leased, repossessed, etc.) ECHEK02Event Checklist - Event #2 ECHEK03 Event Checklist - Event #3 ECHEK04Event Checklist - Event #4 ECHEK05 Event Checklist - Event #5 ECHEK06Event Checklist - Event #6 ECHEK07 Event Checklist - Event #7 ECHEK08Event Checklist - Event #8 ECHEK09 Event Checklist - Event #9 ECHEK10Event Checklist - Event #10

TABLE 2 EVENT VARIABLES EMISSION Vehicle has gone through an emissioninspection, defined by ECHEK01 through ECHEK30 = ‘3030’ GOVUSE Vehiclewas used by a government agency defined by ECHEK01 through ECHEK30 =‘5030’ POLICE Vehicle was used by a police agency defined by ECHEK01through ECHEK30 = ‘5040’ ACCIDENT Accident records were found for theVehicle, defined by ECHEK01 through ECHEK30 = ‘3000’ THEFT Vehicle wasstolen, Insurance claim filed, Auction announced as stolen, etc. definedby ECHEK01 through ECHEK30 = ‘3090’ REPOSS Repossessed Vehicle definedby ECHEK01 through ECHEK30 = ‘5080’ TAXI Vehicle is or was used as ataxi, defined by ECHEK01 through ECHEK30 = ‘5050’ LIVERY Vehicle is “forhire” to transport people, defined by ECHEK01 through ECHEK30 = ‘5020’

Additional factors may be derived through the use of these primaryfactors. The derived factors, as in this example embodiment, can includethe age of the vehicle, maximum mileage, date of last mileage reading,time since that mileage reading, estimated mileage since last reading,estimated total mileage, the MSRP ratio, and whether or not a vehiclehas been leased, and are described in Table 3.

TABLE 3 VARIABLES TO CREATE FOR MODELS CURRYR Current Year CURRMTHCurrent Month AGE Age of Vehicle MAXEMILE Maximum Mileage based uponOdometer reading MAXEYR Event Year associated with Maximum Odometerreading MAXEMTH Event Month associate with Maximum Odometer reading NOMNumber of Months for Mileage Update UPMILES Monthly miles to updateFINMILE Sum of Maximum Odometer reading and Updated miles (UPMILES*NOM)MSRP RATIO Value of Loan/Value of MSRP LEASE Defined by LEASE01 throughLEASE10 = ‘Y’

As can be seen from Table 3, an algorithm may be used to estimate thenumber of miles since the last reported mileage event. The followingalgorithm details a process for estimating mileage in cases where timehas elapsed between the last recorded odometer reading and the present(or the time for which a score is desired). In this embodiment, theestimation is based on the state and zip codes where the car isregistered, and, presumptively, most used. It has been determined thatthe location of use may provide a relatively good approximation of themileage driven over certain periods of time.

Below are descriptions and coding specifications for creating the tableto update AutoCheck mileage based upon the Event History data. Themileage is updated based on each event reported that has a correspondingodometer/mileage reading. The state and ZIP Code where the eventoccurred, as well as ZIPLOC, a Zip locality variable, are used in theupdate process.

First, a sample set of VINs is used and event information is gatheredfor each VIN. Each event with an odometer reading greater than zero issorted by date. Each VIN then gets a counter variable for the number ofqualifying odometer events.

A number of variables are then created, including CURRYR, the currentyear, and CURRMTH, the current month. Each event then gets a count ofthe number of months from the event to the current month(NOM1=(CURRYR−EVENT DATE YEAR−1)*12+(CURRMTH+12−EVENT DATE MONTH).Additionally, the number of miles for each event is then calculated(EVENT_MILES is the Odometer reading for the next future listed eventminus the Odometer reading for the current event). The number of months(NOM) between events is also then similarly calculated. This data isused to create an average number of miles per month for each event (IfNOM greater than 0, MILEMTH=ROUND(EVENT_MILE/NOM)).

If information on the State for the event is unavailable, STATE=“00.”The ZIPLOC variable is also recoded as follows:

If ZIPLOC equals ‘B2’ then ZIPLOC=1 [business]

If ZIPLOC equals ‘C1’, ‘C2’, ‘C3’, ‘C4’ then set ZIPLOC=2 [city]

If ZIPLOC equals ‘R0’, ‘R2’, ‘R3’, ‘R4’ then set ZIPLOC=3 [rural]

If ZIPLOC equals ‘R5’, ‘R6’, ‘R7’, ‘R8’, ‘R9’ then set ZIPLOC=4

If ZIPLOC equals ‘S0’, ‘S1’, ‘S2’, ‘S3’, ‘S4’ then set ZIPLOC=5[suburban]

If ZIPLOC equals ‘S5’, ‘S6’, ‘S7’, ‘S8’, ‘S9’ then set ZIPLOC=6

Else set ZIPLOC=7

Finally a lookup table is created. In an embodiment, this is based on aset number of general rules. The updated mileage table should be basedupon the last six years. Older events should be factored out. Calculatethe average miles per month for each STATE and ZIP LOCALITY combined aswell as each State. Evaluate the sample size for each STATE and ZIPLOCALITY. If the sample size is less than 100, then replace averageusing a similar state. For example, cars in North Dakota in Business(B2) Zip Codes might be replaced with the average monthly miles forvehicles in South Dakota with Business Zip Codes. Replace all missingZip Localities (those coded to the value ‘7’) with the average monthlymiles for the state.

Based upon the most recent owner's State and Zip Locality, the updatemileage variable (FINMILES) can then be determined from the table(FINMILES=NOM*UPMILES+MAXEMILES). Table 4 is an example of the tableused.

TABLE 4 UPDATE MILEAGE LOOK-UP TABLE State Zip Locality Final MilesMissing 1 1483.12 Missing 2 5020.58 Missing 3 4275.67 Missing 4 1483.12Missing 5 3604.38 Missing 6 1483.12 Missing 7 1377.39 AA 2 1483.12 AA 71483.12 AB 7 1483.12 AE 7 1483.12 AK 1 1311.42 AK 2 1232.92 AK 3 1092.14AK 4 1225.94 AK 5 1086.66 AK 6 1573.99 AK 7 1048.88 AL 1 1582.03 AL 21796.36 AL 3 1845.35 AL 4 1920.83 AL 5 1848.28 AL 6 1907.61 AL 7 1768.34AP 7 1483.12 AR 1 2169.12 AR 2 2106.40 AR 3 2024.48 AR 4 2272.49 AR 52518.71 AR 6 2198.32 AR 7 2094.09 AZ 1 2082.01 AZ 2 1579.05 AZ 3 1360.49AZ 4 1776.14 AZ 5 1504.59 AZ 6 1804.62 AZ 7 1771.60 CA 1 1554.46 CA 21748.52 CA 3 1411.67 CA 4 2077.00 CA 5 1546.35 CA 6 1587.49 CA 7 1226.67CO 1 1541.59 CO 2 1880.47 CO 3 2025.40 CO 4 2160.06 CO 5 1648.34 CO 62322.54 CO 7 1728.68 CT 1 1246.95 CT 2 1312.29 CT 3 1456.99 CT 4 1422.18CT 5 1343.64 CT 6 1379.56 CT 7 1460.49 DC 1 1475.79 DC 2 3513.60 DC 42192.27 DC 5 1689.88 DC 6 1956.93 DC 7 1678.53 DE 1 1963.05 DE 2 1756.44DE 3 1776.50 DE 4 1800.10 DE 5 1989.94 DE 6 1611.47 DE 7 2062.76 FL 11400.08 FL 2 1451.88 FL 3 1886.91 FL 4 1541.97 FL 5 1491.59 FL 6 1538.05FL 7 1529.43 GA 1 2426.22 GA 2 2048.94 GA 3 2092.61 GA 4 2472.52 GA 52059.61 GA 6 2079.48 GA 7 1885.64 GU 7 1483.12 HI 1 1673.82 HI 2 1283.69HI 3 1321.29 HI 4 1848.58 HI 5 1649.42 HI 6 1464.42 HI 7 1464.42 IA 11823.26 IA 2 1241.00 IA 3 1165.85 IA 4 1479.03 IA 5 1343.85 IA 6 1434.76IA 7 1301.97 ID 1 956.95 ID 2 1688.02 ID 3 2504.72 ID 4 2510.55 ID 51392.08 ID 6 2230.52 ID 7 1315.32 IL 1 2322.06 IL 2 1521.42 IL 3 1533.54IL 4 1993.73 IL 5 1478.86 IL 6 1835.36 IL 7 1522.20 IN 1 1042.08 IN 21241.58 IN 3 1298.56 IN 4 1580.42 IN 5 1512.47 IN 6 1582.15 IN 7 1391.91KS 1 1330.02 KS 2 2132.75 KS 3 1699.21 KS 4 2066.43 KS 5 1837.87 KS 62663.32 KS 7 1910.31 KY 1 1996.76 KY 2 2339.08 KY 3 1993.62 KY 4 2712.68KY 5 2437.63 KY 6 2567.52 KY 7 2261.82 LA 1 1559.27 LA 2 1675.22 LA 31731.62 LA 4 1817.26 LA 5 1587.92 LA 6 2051.50 LA 7 1666.98 MA 1 1335.57MA 2 1619.84 MA 3 1404.43 MA 4 1389.55 MA 5 1377.12 MA 6 1543.68 MA 71610.56 MD 1 1963.05 MD 2 2684.51 MD 3 2686.33 MD 4 2997.66 MD 5 2700.78MD 6 2829.75 MD 7 2558.74 ME 1 1418.26 ME 2 1563.93 ME 3 2347.69 ME 42694.69 ME 5 2426.83 ME 6 2304.48 ME 7 2153.56 MI 1 1117.48 MI 2 1305.62MI 3 1196.14 MI 4 1435.20 MI 5 1230.84 MI 6 1360.35 MI 7 1227.71 MN 11613.47 MN 2 1652.03 MN 3 1470.40 MN 4 1865.70 MN 5 1463.86 MN 6 1640.23MN 7 1318.35 MO 1 1539.53 MO 2 1745.38 MO 3 1939.02 MO 4 1956.37 MO 51807.58 MO 6 1823.52 MO 7 1724.75 MP 7 1483.12 MS 1 1559.27 MS 2 1704.64MS 3 2008.57 MS 4 2346.50 MS 5 2058.47 MS 6 2151.04 MS 7 2068.00 MT 1956.95 MT 2 1282.64 MT 3 1208.02 MT 4 1596.97 MT 5 1392.08 MT 6 2230.52MT 7 1630.64 NC 1 1574.96 NC 2 1621.88 NC 3 1625.27 NC 4 1558.11 NC 51575.37 NC 6 1683.43 NC 7 1409.02 ND 1 1515.62 ND 2 1415.50 ND 3 1149.02ND 4 1679.84 ND 5 1438.63 ND 6 1432.23 ND 7 1456.36 NE 1 1269.73 NE 21087.68 NE 3 1079.27 NE 4 1333.34 NE 5 1348.08 NE 6 1288.48 NE 7 1149.58NH 1 1418.26 NH 2 1348.63 NH 3 1463.91 NH 4 1583.05 NH 5 1436.37 NH 61638.45 NH 7 1466.55 NJ 1 1296.28 NJ 2 1573.06 NJ 3 1888.12 NJ 4 2001.17NJ 5 1191.23 NJ 6 1332.83 NJ 7 1260.33 NM 1 2943.53 NM 2 1951.06 NM 31834.20 NM 4 2980.27 NM 5 2368.07 NM 6 2875.31 NM 7 1752.88 NV 1 1380.88NV 2 1433.45 NV 3 1360.49 NV 4 1776.14 NV 5 1369.45 NV 6 1804.62 NV 71220.30 NY 1 1385.56 NY 2 1822.65 NY 3 1888.12 NY 4 2001.17 NY 5 1616.29NY 6 1773.74 NY 7 1616.57 OH 1 1303.60 OH 2 1426.96 OH 3 1168.74 OH 42011.73 OH 5 1243.44 OH 6 1937.37 OH 7 1372.57 OK 1 2112.93 OK 2 1970.50OK 3 1583.15 OK 4 2142.48 OK 5 2134.95 OK 6 2666.83 OK 7 1941.04 ON 71483.12 OR 1 1290.17 OR 2 1401.47 OR 3 1575.53 OR 4 1670.23 OR 5 1439.28OR 6 1423.45 OR 7 1544.29 PA 1 1818.83 PA 2 1422.47 PA 3 1379.61 PA 41566.14 PA 5 1476.00 PA 6 1568.76 PA 7 1338.45 PR 3 1483.12 PR 7 1483.12RI 1 1483.12 RI 2 1483.12 RI 5 1483.12 RI 6 1483.12 RI 7 1379.00 SC 13105.56 SC 2 2543.41 SC 3 2527.49 SC 4 2425.91 SC 5 2573.69 SC 6 2730.64SC 7 2458.53 SD 1 1203.13 SD 2 1157.69 SD 3 1203.30 SD 4 1563.26 SD 51474.27 SD 6 1432.23 SD 7 1249.04 TN 1 1675.55 TN 2 1785.01 TN 3 1660.02TN 4 1723.65 TN 5 1818.94 TN 6 1965.45 TN 7 1696.79 TX 1 1642.74 TX 21766.24 TX 3 1619.07 TX 4 1954.60 TX 5 1738.27 TX 6 1831.68 TX 7 1500.61UT 1 1541.59 UT 2 2280.45 UT 3 2025.40 UT 4 2160.06 UT 5 2194.22 UT 62322.54 UT 7 1580.50 VA 1 1475.79 VA 2 1792.64 VA 3 1954.60 VA 4 2192.27VA 5 1689.88 VA 6 1956.93 VA 7 1678.53 VT 1 1418.26 VT 2 1348.63 VT 32626.03 VT 4 2575.36 VT 5 1436.37 VT 6 1638.45 VT 7 2066.08 WA 1 1290.17WA 2 1401.47 WA 3 1575.53 WA 4 1462.90 WA 5 1439.28 WA 6 1423.45 WA 71307.38 WI 1 1050.09 WI 2 2105.45 WI 3 1807.89 WI 4 1763.28 WI 5 1585.15WI 6 2034.03 WI 7 1610.20 WV 1 2756.06 WV 2 1479.96 WV 3 1884.93 WV 41670.23 WV 5 1603.53 WV 6 1802.25 WV 7 1728.80 WY 1 1670.87 WY 2 1354.22WY 3 1577.46 WY 4 2000.25 WY 5 1474.27 WY 6 1432.23 WY 7 1547.65 MissingMissing 1483.12

The other derived variables created are the Current Year (CURRYR) andCurrent Month (CURRMTH), as well as an automobiles age(AGE=CURRYR−MODELYR). Due to the fact that, in some embodiments, modelyears do not necessarily coincide exactly with calendar years, if theabove calculation of AGE equals −1, the AGE is set to 0, and if the AGEequals CURRYR (the model year is missing or unknown for some reason),then the earliest year available from the event history is used as aproxy for MODELYR, and AGE is recalculated. As described above, in theexample embodiment, six age categories (designated by AGE1) are used:

If AGE is greater than or equal to 0 and less than or equal to 3 thenset AGE1=1

If AGE is greater than or equal to 4 and less than or equal to 5 thenset AGE1=2

If AGE is greater than or equal to 6 and less than or equal to 8 thenset AGE1=3

If AGE is greater than or equal to 9 and less than or equal to 10 thenset AGE1=4

If AGE is greater than or equal to 11 and less than or equal to 12 thenset AGE1=5

If AGE is greater than or equal to 13 then set AGE1=6

ZIPLOC is set based on the current owner's zip locality:

If ZIPLOC1 equals ‘B2’ then set ZIPLOC=1

If ZIPLOC1 equals ‘C1’, ‘C2’, ‘C3’, ‘C4’ then set ZIPLOC=2

If ZIPLOC1 equals ‘R0’, ‘R2’, ‘R3’, ‘R4’ then set ZIPLOC=3

If ZIPLOC1 equals ‘R5’, ‘R6’, ‘R7’, ‘R8’, ‘R9’ then set ZIPLOC=4

If ZIPLOC1 equals ‘S0’, ‘S1’, ‘S2’, ‘S3’, ‘S4’ then set ZIPLOC=5

If ZIPLOC1 equals ‘S5’, ‘S6’, ‘S7’, ‘S8’, ‘S9’ then set ZIPLOC=6

Else set ZIPLOC=7

If the current owner's state is missing, STATE is coded to ‘00.’ Themaximum miles can be computed as the maximum of all event odometerreadings. The corresponding event year (MAXEYR) and event month(MAXEMTH) of the maximum odometer reading should be passed to two newvariables. Using STATE1 and ZIPLOC1, the table discussed above can givethe value of UPMILES. Finally, if VALLOAN is greater than 0 AND VALMSRPis greater than 0, then MSRPRAT=VALLOAN/VALMSRP. Once these variablesare known, the automobile or automobiles being scored can be filteredinto the correct age groups, and scored as below:

1. SEGMENT 1: Age 0-3 Years

-   -   Select if AGE1=1

Recode and Point Assignment to FINMILE:

-   -   If AGE=0 and FINMILE is blank then set FINMILE=14510.17    -   If AGE=1 and FINMILE is blank then set FINMILE=29565.12    -   If AGE=2 and FINMILE is blank then set FINMILE=48072.77    -   If AGE=3 and FINMILE is blank then set FINMILE=64491.77    -   Compute FINMILE=FINMILE*−0.000005

Point Assignment to TOTOWN1:

-   -   Compute TOTOWN1=TOTOWN1*−0.0894

Recode and Point Assignment to HASSURED: Autocheck Assured

-   -   If HASSURED=‘Y’ then set HASSURED=0.4319    -   Else set HASSURED=−0.4319

Recode and Point Assignment to NEGA1: (Government Use, Police Use,Accident, Theft)

-   -   Count GOVUSE=ECHEK01 to ECHEK30 (‘5030’)    -   Count POLICE=ECHEK01 to ECHEK30 (‘5040’)    -   Count ACCIDENT=ECHEK01 to ECHEK30 (‘3000’)    -   Count THEFT=ECHEK01 to ECHEK30 (‘3090’)

Recode GOVUSE POLICE ACCIDENT THEFT (1 Thru hi=1).

-   -   If GOVUSE, POLICE, ACCIDENT or THEFT=1 then set NEGA1=−0.4216    -   Else set NEGA1=0.4216

Recode and Point Assignment to MAKETXT:

-   -   If MAKETXT=‘Aston Martin’, ‘Ferrari’, ‘Lotus’, ‘Hummer’, ‘BMW’,        ‘Mini’, ‘Jaguar’, ‘Subaru’, ‘Rolls Royce’, ‘Bentley’, ‘Lexus’,        ‘Lamborghini’        -   then set MAKETXT=0.2622    -   If MAKETXT=‘Infiniti’, ‘Mercedes-Benz’, ‘Cadillac’, ‘Buick’,        ‘Volvo’, ‘Porsche’, ‘Saab’        -   then set MAKETXT=0.2243    -   If MAKETXT=‘Audi’, ‘Acura’, ‘Toyota’, ‘Scion’, ‘Lincoln’,        ‘Honda’, ‘Chrysler’, ‘Volkswagen’, ‘Jeep’, ‘Land Rover’        -   then set MAKETXT=0.1613    -   If MAKETXT=‘Nissan’, ‘GMC’, ‘Oldsmobile’, ‘Chevrolet’, ‘Saturn’,        ‘Pontiac’, ‘Dodge’, ‘Dodge Freightliner’, ‘Freightliner’,        ‘Ford’, ‘Mercury’        -   then set MAKETXT=−0.0022    -   If MAKETXT=‘Mazda’, ‘Isuzu’, ‘Mitsubishi’, ‘Plymouth’, ‘Hyundai’        -   then set MAKETXT=−0.2109    -   If MAKETXT=‘Kia’, ‘Daewoo’, ‘Suzuki’, ‘Eagle’        -   then set MAKETXT=−0.4347    -   If MAKETXT is blanks then set MAKETXT=−0.0022

Recode and Point Assignment to VEHCLASS:

-   -   If VEHCLASS=‘Sport Car—Ultra Luxury’, ‘Sport Wagon—Mid Range’,        ‘Sport Car—Premium’, ‘Upscale—Premium’, ‘Upscale—Near Luxury’        ‘Upscale—Luxury’, ‘Sport Car—Upper Premium’, ‘SUV—Large’,        ‘SUV—Upper Mid Range’, ‘Mid Range Car—Premium’        -   then set VEHCLASS=0.3441    -   If VEHCLASS=‘SUV—Pickup’, ‘SUV—Lower Mid Range’, ‘CUV—Mid        Range’, ‘Van—Mini’, ‘Pickup—Full Sized’, ‘Mid Range        Car—Standard’, ‘SUV—Premium Large’, ‘CUV—Premium’, ‘Sport        Wagon—Premium’, ‘Upscale—Ultra’, ‘Sport Wagon—Entry Level’, ‘Alt        Power—Hybrid Car’, ‘SUV—Entry Level’, ‘CUV—Entry Level’,        ‘Pickup—Small’        -   then set VEHCLASS=0.1067    -   If VEHCLASS=‘Mid Range Car—Lower’, ‘Van—Full Sized’, ‘Sport        Car—Touring’    -   then set VEHCLASS=−0.1285    -   If VEHCLASS=‘Traditional Car’, ‘Small Car—Economy’, ‘Small        Car—Budget’        -   then set VEHCLASS=−0.3223    -   If VEHCLASS is blank then set VEHCLASS=0.1067

Creation of SCORE for Vehicles 0-3 Years Old:

-   -   SCORE=(1.7137+FINMILE+TOTOWN1+HASSURED+NEGA1+MAKETXT+VEHCLASS)

2. SEGMENT 2: Age 4-5 Years

-   -   Select if AGE1=2

Recode and Point Assignment to MSRPRAT:

-   -   If AGE=4 and MSRPRAT is blank then set MSRPRAT=0.4771    -   If AGE=5 and MSRPRAT is blank then set MSRPRAT=0.3901    -   Compute MSRPRAT=MSRPRAT*1.0794

Recode and Point Assignment to FINMILE:

-   -   If AGE=4 and FINMILE is blank then set FINMILE=80945.76    -   If AGE=5 and FINMILE is blank then set FINMILE=96516.11    -   Compute FINMILE=FINMILE*−0.000006

Point Assignment to TOTOWN1:

-   -   Compute TOTOWN1=TOTOWN1*−0.1191

Recode and Point Assignment to HASSURED:

-   -   If HASSURED equals ‘Y’ then set HASSURED=0.2872    -   Else set HASSURED equal to −0.2872

Create and Point Assignment to POS1:

-   -   Count LEASE=LEASE01 to LEASE10 (‘Y’)    -   Count EMISSION=ECHEK01 to ECHEK30 (‘3030’)

Recode LEASE EMISSION (1 Thru hi=1)

-   -   If LEASE or EMISSION equals 1 then set then set POS1=0.0455    -   Else set POS1=−0.0455

Create and Point Assignment to NEGB1:

-   -   Count ACCIDENT=ECHEK01 to ECHEK30 (‘3000’)    -   Count THEFT=ECHEK01 to ECHEK30 (‘3090’)    -   Count REPOSS=ECHEK01 to ECHEK30 (‘5080’)    -   Count TAXI=ECHEK01 to ECHEK30 (‘5050’)

Recode ACCIDENT THEFT REPOSS TAXI (1 Thru hi=1)

-   -   If ACCIDENT, THEFT, REPOSS or TAXI=1 then set NEGB1=−0.1591    -   Else set NEGB1=0.1591

Recode and Point Assignment to MAKETXT:

-   -   If MAKETXT=‘Lotus’, ‘Rolls Royce’, ‘BMW’, ‘Mini’, ‘Ferrari’,        ‘Volvo’, ‘Mercedes-Benz’, ‘Bentley’, ‘Lexus’, ‘Subaru’        -   then set MAKETXT=0.2640    -   If MAKETXT=‘Jaguar’, ‘Porsche’, ‘GMC’, ‘Audi’, ‘Lincoln’,        ‘Saab’, ‘Cadillac’, ‘Buick’        -   then set MAKETXT=0.1283    -   If MAKETXT=‘Jeep’, ‘Honda’, ‘Infiniti’, ‘Acura’, ‘Toyota’,        ‘Scion’, ‘Land Rover’        -   then set MAKETXT=0.1022    -   If MAKETXT=‘Chevrolet’, ‘Hummer’, ‘Ford’, ‘Oldsmobile’, ‘Isuzu’,        ‘Chrysler’, ‘Volkswagen’, ‘Dodge’, ‘Dodge Freightliner’,        ‘Freightliner’, ‘Saturn’        -   then set MAKETXT=−0.0764    -   If MAKETXT=‘Nissan’, ‘Mercury’, ‘Mazda’, ‘Pontiac’,        ‘Mitsubishi’, ‘Plymouth’        -   then set MAKETXT=−0.1417    -   If MAKETXT=‘Daewoo’, ‘Eagle’, ‘Geo’, ‘Kia’, ‘Suzuki’, ‘Hyundai’        -   then set MAKETXT=−0.2764    -   If MAKETXT=blank then set MAKETXT=0.1022

Recode and Point Assignment to VEHCLASS:

-   -   If VEHCLASS=‘Alt Power—Hybrid Car’, ‘Alt Power—Hybrid Truck’,        ‘Sport Car—Ultra Luxury’, ‘Sport Car—Upper Premium’        -   then set VEHCLASS=0.7330    -   If VEHCLASS=‘Upscale—Premium’, ‘Pickup—Full Sized’, ‘SUV—Large’,        ‘Upscale—Luxury’, ‘Upscale—Near Luxury’, ‘SUV—Premium Large’,        ‘CUV—Premium’, ‘SUV—Upper Mid Range’        -   then set VEHCLASS=0.1891    -   If VEHCLASS=‘SUV—Lower Mid Range’, ‘SUV—Mid Range’, ‘Van—Full        Sized’, ‘Mid Range Car—Premium’, ‘Sport Car—Premium’, ‘SUV—Entry        Level’, ‘CUV—Entry Level’        -   then set VEHCLASS=0.0594    -   If VEHCLASS=‘Pickup—Small’, ‘Sport Wagon—Entry Level’,        ‘Traditional Car’, ‘Van—Mini’, ‘Mid Range Car—Standard’        -   then set VEHCLASS=−0.1203    -   If VEHCLASS=‘Upscale—Ultra’, ‘Sport Car—Touring’        -   then set VEHCLASS=−0.4122    -   If VEHCLASS=‘Mid Range Car—Lower’, ‘Small Car—Economy’, ‘Small        Car—Budget’        -   then set VEHCLASS=−0.4490    -   If VEHCLASS=blank then set VEHCLASS=0.0594

Creation of SCORE for Vehicles 4-5 Years Old:

-   -   SCORE=(1.9333+MSRPRAT+FINMILE+TOTOWN1+HASSURED+POS1+NEGB1+MAKETXT+VEHCLASS)

3. SEGMENT 3: Age 6-8 Years

-   -   Select if AGE1=3

Recode and Point Assignment to MSRPRAT:

-   -   If AGE=6 and MSRPRAT is blank then set MSRPRAT=0.3141    -   If AGE=7 and MSRPRAT is blank then set MSRPRAT=0.2565    -   If AGE=8 and MSRPRAT is blank then set MSRPRAT=0.2068    -   Compute MSRPRAT=MSRPRAT*2.3463

Recode and Point Assignment to FINMILE:

-   -   If AGE=6 and FINMILE is blank then set FINMILE=111724.22    -   If AGE=7 and FINMILE is blank then set FINMILE=123938.87    -   If AGE=8 and FINMILE is blank then set FINMILE=136387.33    -   Compute FINMILE=FINMILE*−0.000006

Point Assignment to TOTOWN1:

-   -   Compute TOTOWN1=TOTOWN1*−0.1569

Recode and Point Assignment to HASSURED:

-   -   If HASSURED equals ‘Y’ then set HASSURED=0.2865    -   Else set HASSURED equal to −0.2865

Create and Point Assignment to POS1:

-   -   Count LEASE=LEASE01 to LEASE10 (‘Y’)    -   Count EMISSION=ECHEK01 to ECHEK30 (‘3030’)

Recode LEASE EMISSION (1 Thru hi=1)

-   -   If LEASE or EMISSION equals 1 then set then set POS1=0.1051    -   Else set POS1=−0.1051

Create and Point Assignment to NEGC1:

-   -   Count ACCIDENT=ECHEK01 to ECHEK30 (‘3000’)    -   Count THEFT=ECHEK01 to ECHEK30 (‘3090’)    -   Count REPOSS=ECHEK01 to ECHEK30 (‘5080’)    -   Count TAXI=ECHEK01 to ECHEK30 (‘5050’)

Recode ACCIDENT THEFT REPOSS TAXI (1 Thru hi=1)

-   -   If ACCIDENT, THEFT, REPOSS or TAXI equals 1 then set        NEGC1=−0.1652    -   Else set NEGC1=0.1652

Recode and Point Assignment to MAKETXT:

-   -   If MAKETXT=‘Lotus’, ‘Rolls Royce’, ‘Porsche’, ‘Ferrari’,        ‘Hummer’, ‘Mercedes-Benz’, ‘Alfa Romeo’, ‘Jaguar’, ‘Bentley’,        ‘BMW’, ‘Mini’        -   then set MAKETXT=0.3857    -   If MAKETXT=‘Volvo’, ‘Lexus’, ‘Land Rover’, ‘Cadillac’, ‘GMC’,        ‘Honda’, ‘Jeep’, ‘Toyota’, ‘Scion’        -   then set MAKETXT=0.1595    -   If MAKETXT=‘Acura’, ‘Buick’, ‘Audi’, ‘Infiniti’, ‘Isuzu’,        ‘Subaru’, ‘Lincoln’, ‘Saab’, ‘Chevrolet’, ‘Oldsmobile’,        ‘Nissan’, ‘Volkswagen’        -   then set MAKETXT=−0.0549    -   If MAKETXT=‘Ford’, ‘Suzuki’, ‘Chrysler’, ‘Saturn’, ‘Dodge’,        ‘Dodge Freightliner’, ‘Freightliner’, ‘Kia’, ‘Mercury’, ‘Mazda’,        ‘Pontiac’, ‘Mitsubishi’ or ‘Geo’        -   then set MAKETXT=−0.1441    -   If MAKETXT=‘Plymouth’, ‘Eagle’, ‘Hyundai’        -   then set MAKETXT=−0.3462    -   If MAKETXT is blank then set MAKETXT=−0.0549

Recode and Point Assignment to VEHCLASS:

-   -   If VEHCLASS is equal ‘Sport Car—Ultra Luxury’, ‘Sport Car—Upper        Premium’, ‘Upscale—Premium’, ‘SUV—Large’, ‘SUV—Premium Large’,        ‘CUV—Premium’, ‘Pickup—Full Sized’        -   then set VEHCLASS=0.3360    -   If VEHCLASS is equal ‘Upscale—Luxury’, ‘SUV—Lower Mid Range’,        ‘CUV—Mid Range’, ‘Upscale—Near Luxury’, ‘Van—Full Sized’,        ‘SUV—Entry Level’, ‘CUV—Entry Level’        -   then set VEHCLASS=0.1652    -   If VEHCLASS=‘Mid Range Car—Premium’, ‘Pickup—Small’,        ‘Traditional Car’, ‘SUV—Upper Mid Range’, ‘Mid Range        Car—Standard’, ‘Sport Car—Premium’        -   then set VEHCLASS=−0.0474    -   If VEHCLASS=‘Van—Mini’, ‘Sport Car—Touring’        -   then set VEHCLASS=−0.1117    -   If VEHCLASS=‘Mid Range Car—Lower’, ‘Small Car—Economy’,        ‘Upscale—Ultra’, ‘Small Car—Budget’        -   then set VEHCLASS=−0.3421    -   If VEHCLASS=blank then set VEHCLASS=−0.0474

Creation of SCORE for Vehicles 6-8 Years Old:

-   -   SCORE=(1.4112+MSRPRAT+FINMILE+TOTOWN1+HASSURED+POS1+NEGC1+MAKETXT+VEHCLASS)

4. SEGMENT 4: Age 9-10 Years

-   -   Select if AGE1=4

Recode and Point Assignment to MSRPRAT:

-   -   If AGE=9 and MSRPRAT is blank then set MSRPRAT=0.1949    -   If AGE=10 and MSRPRAT is blank then set MSRPRAT=0.1749    -   Compute MSRPRAT=MSRPRAT*2.0448

Recode and Point Assignment to FINMILE:

-   -   If AGE=9 and FINMILE is blank then set FINMILE=147029.5    -   If AGE=10 and FINMILE is blank then set FINMILE=157867.1    -   Compute FINMILE=FINMILE*−0.000004

Point Assignment to TOTOWN1:

-   -   Compute TOTOWN1=TOTOWN1*−0.1717

Recode and Point Assignment to HASSURED:

-   -   If HASSURED equals ‘Y’ then set HASSURED=0.3086    -   Else set HASSURED equal to −0.3086

Create and Point Assignment to POS1:

-   -   Count LEASE=LEASE01 to LEASE10 (‘Y’)    -   Count EMISSION=ECHEK01 to ECHEK30 (‘3030’)    -   Recode LEASE EMISSION (1 thru hi=1)    -   If LEASE or EMISSION equals 1 then set then set POS1=0.1495    -   Else set POS1=−0.1495

Create and Point Assignment to NEGD1:

-   -   Count ACCIDENT=ECHEK01 to ECHEK30 (‘3000’)    -   Count THEFT=ECHEK01 to ECHEK30 (‘3090’)    -   Count REPOSS=ECHEK01 to ECHEK30 (‘5080’)    -   Count TAXI=ECHEK01 to ECHEK30 (‘5050’)    -   Recode ACCIDENT THEFT REPOSS TAXI (1 thru hi=1)    -   If ACCIDENT, THEFT, REPOSS or TAXI equals 1 then set        NEGD1=−0.1971    -   Else set NEGD1=0.1971

Create and Point Assignment to NEGD2:

-   -   Count POLICE=ECHEK01 to ECHEK30 (‘5040’)    -   Count GOVUSE=ECHEK01 to ECHEK30 (‘5030’)    -   Count LIVERY=ECHEK01 to ECHEK30 (‘5020’)    -   Recode POLICE GOVUSE LIVERY (1 thru hi=1)    -   If POLICE, GOVUSE or LIVERY equals 1 then set NEGD1=−0.3911    -   Else set NEGD1=0.3911

Recode and Point Assignment to MAKETXT:

-   -   If MAKETXT=‘Aston Martin’, ‘Ferrari’, ‘Hummer’, ‘Lamborghini’,        ‘Rolls Royce’, ‘Porsche’, ‘Mercedes-Benz’, ‘Bentley’, ‘Lexus’,        ‘BMW’, ‘Mini’        -   then set MAKETXT=0.6189    -   If MAKETXT=‘Volvo’, ‘Jeep’, ‘Lotus’, ‘Jaguar’, ‘Acura’, ‘Honda’,        ‘Alfa Romeo’, ‘Toyota’, ‘Scion’, ‘T.C.’        -   then set MAKETXT=0.3908    -   If MAKETXT=‘Land Rover’, ‘Cadillac’, ‘GMC’, ‘Infiniti’, ‘Buick’        -   then set MAKETXT=0.0264    -   If MAKETXT=‘Audi’, ‘Saab’, ‘Suzuki’, ‘Lincoln’, ‘Nissan’,        ‘Chevrolet’, ‘Mazda’, ‘Subaru’, ‘Chrysler’, ‘Oldsmobile’,        ‘Isuzu’, ‘Daihatsu’, ‘Ford’, ‘Dodge’, ‘Dodge Freightliner’,        ‘Freightliner’, ‘Saturn’, ‘Volkswagen’        -   then set MAKETXT=−0.2242    -   If MAKETXT=‘Mercury’, ‘Mitsubishi’, ‘Peugeot’        -   then set MAKETXT=−0.3677    -   If MAKETXT=‘Pontiac’, ‘Geo’, ‘Plymouth’, ‘Eagle’, ‘Hyundai’,        ‘Sterling’, ‘Yugo’        -   then set MAKETXT=−0.4442    -   If MAKETXT is blank then set MAKETXT=0.0264

Recode and Point Assignment to VEHCLASS:

-   -   If VEHCLASS=‘Upscale—Ultra’, ‘Sport Car—Ultra Luxury’, ‘Sport        Car—Upper Premium’, ‘SUV—Premium Large’, ‘CUV—Premium’,        ‘SUV—Large’, ‘Pickup—Full Sized’, ‘Upscale—Premium’        -   then set VEHCLASS=0.4292    -   If VEHCLASS=‘Upscale—Near Luxury’, ‘Upscale—Luxury’, ‘SUV—Entry        Level’, ‘CUV—Entry Level’        -   then set VEHCLASS=0.2013    -   If VEHCLASS=‘SUV—Upper Mid Range’, ‘Van—Full Sized’, ‘SUV—Lower        Mid Range’, ‘CUV—Mid Range’, ‘Sport Car—Premium’        -   then set VEHCLASS=0.0728    -   If VEHCLASS=‘Mid Range Car—Premium’, ‘Traditional Car’,        ‘Pickup—Small’        -   then set VEHCLASS=−0.0625    -   If VEHCLASS=‘Mid Range Car—Standard’, ‘Sport Car—Touring’        -   then set VEHCLASS=−0.1860    -   If VEHCLASS=‘Van—Mini’, ‘Mid Range Car—Lower’, ‘Small        Car—Economy’, ‘Small Car—Budget’        -   then set VEHCLASS=−0.4548    -   If VEHCLASS=blank then set VEHCLASS=0.0728

Creation of SCORE for Vehicles 9-10 Years Old:

-   -   SCORE=(0.6321+MSRPRAT+FINMILE+TOTOWN1+HASSURED+POS1+NEGD1+NEGD2+MAKETXT+VEHCLASS)

5. SEGMENT 5: Age 11-12 Years

-   -   Select if AGE1=5

Recode and Point Assignment to MSRPRAT:

-   -   If AGE=11 and MSRPRAT is blank then set MSRPRAT=0.1446    -   If AGE=12 and MSRPRAT is blank then set MSRPRAT=0.1248    -   Compute MSRPRAT=MSRPRAT*3.7191

Recode and Point Assignment to FINMILE:

-   -   If AGE=11 and FINMILE is blank then set FINMILE=169523.67    -   If AGE=12 and FINMILE is blank then set FINMILE=179620.44    -   Compute FINMILE=FINMILE*−0.000003

Point Assignment to TOTOWN1:

-   -   Compute TOTOWN1=TOTOWN1*−0.3131

Recode and Point Assignment to HASSURED:

-   -   If HASSURED equals ‘Y’ then set HASSURED=0.2076    -   Else set HASSURED equal to −0.2076

Create and Point Assignment to POS1:

-   -   Count LEASE=LEASE01 to LEASE10 (‘Y’)    -   Count EMISSION=ECHEK01 to ECHEK30 (‘3030’)

Recode LEASE EMISSION (1 Thru hi=1)

-   -   If LEASE or EMISSION equals 1 then set then set POS1=0.2573    -   Else set POS1=−0.2573

Create and Point Assignment to NEGE1:

-   -   Count ACCIDENT=ECHEK01 to ECHEK30 (‘3000’)    -   Count THEFT=ECHEK01 to ECHEK30 (‘3090’)    -   Count REPOSS=ECHEK01 to ECHEK30 (‘5080’)    -   Count TAXI=ECHEK01 to ECHEK30 (‘5050’)

Recode ACCIDENT THEFT REPOSS TAXI (1 Thru hi=1)

-   -   If ACCIDENT, THEFT, REPOSS or TAXI equals 1 then set        NEGE1=−0.2057    -   Else set NEGE1=0.2057

Recode and Point Assignment to MAKETXT:

-   -   If MAKETXT=‘Ferrari’, ‘Lamborghini’, ‘Lotus’, ‘Rolls Royce’,        ‘Alfa Romeo’, ‘Bentley’, ‘Porsche’, ‘Mercedes-Benz’, ‘Lexus’,        ‘BMW’, ‘Mini’, ‘Laforza’, ‘Jaguar’, ‘Land Rover’, ‘Volvo’,        ‘Jeep’        -   then set MAKETXT=0.6174    -   If MAKETXT=‘GMC’, ‘Infiniti’, ‘Toyota’, ‘Scion’, ‘Acura’        -   then set MAKETXT=0.1136    -   If MAKETXT=‘Honda’, ‘Cadillac’, ‘Sterling’ ‘Isuzu’, ‘Daihatsu’,        ‘Mazda’, ‘Chevrolet’, ‘Buick’, ‘Ford’, ‘Nissan’ ‘Lincoln’,        ‘Volkswagen’, ‘Oldsmobile’, ‘Suzuki’, ‘Audi’, ‘T.C.’, ‘Saab’,        ‘Avanti’        -   then set MAKETXT=−0.0944    -   If MAKETXT=‘Mitsubishi’, ‘Dodge’, ‘Dodge Freightliner’,        ‘Freightliner’, ‘Chrysler’, ‘Geo’, ‘Subaru’        -   then set MAKETXT=−0.1559    -   If MAKETXT=‘Mercury’, ‘Peugeot’, ‘Pontiac’, ‘Plymouth’,        ‘Hyundai’, ‘Merkur’, ‘Eagle’, ‘AMC’, ‘Yugo’, ‘GMC Canada’        -   then set MAKETXT=−0.4807    -   If MAKETXT is blank then set MAKETXT=−0.0944

Recode and Point Assignment to VEHCLASS:

-   -   If VEHCLASS=‘Sport Car—Ultra Luxury’, ‘Upscale—Ultra’,        ‘SUV—Premium Large’, ‘CUV—Premium’, ‘Upscale—Premium’, ‘Sport        Car—Upper Premium’, ‘Pickup—Full Sized’, ‘SUV—Lower Mid Range’,        ‘CUV—Mid Range’, ‘SUV—Large’        -   then set VEHCLASS=0.3976    -   If VEHCLASS=‘SUV—Entry Level’, ‘CUV—Entry Level’,        ‘Upscale—Luxury’, ‘Van—Full Sized’, ‘Pickup—Small’, ‘Sport        Car—Premium’, ‘Upscale—Near Luxury’        -   then set VEHCLASS=0.1455    -   If VEHCLASS=‘Traditional Car’, ‘Mid Range Car—Premium’, ‘Sport        Car—Touring’        -   then set VEHCLASS=0.0066    -   If VEHCLASS=‘Mid Range Car—Standard’, ‘Van—Mini’        -   then set VEHCLASS=−0.1662    -   If VEHCLASS=‘Mid Range Car—Lower’, ‘Small Car—Budget’, ‘Small        Car—Economy’, ‘Sport Wagon—Entry Level’        -   then set VEHCLASS=−0.3835    -   If VEHCLASS=blank then set VEHCLASS=0.0066

Creation of SCORE for Vehicles 11-12 Years Old:

-   -   SCORE=(0.5500+MSRPRAT+FINMILE+TOTOWN1+HASSURED+POS1+NEGE1+MAKETXT+VEHCLASS)

6. SEGMENT 6: Age 13+Years

-   -   Select if AGE1=6

Recode and Point Assignment to MSRPRAT:

-   -   If AGE=13 and MSRPRAT is blank then set MSRPRAT=0.1024    -   If AGE=14 and MSRPRAT is blank then set MSRPRAT=0.0884    -   If AGE=15 and MSRPRAT is blank then set MSRPRAT=0.0727    -   If AGE>=16 and MSRPRAT is blank then set MSRPRAT=0.0001    -   Compute MSRPRAT=MSRPRAT*3.7223

Recode and Point Assignment to FINMILE:

-   -   If AGE=13 and FINMILE is blank then set FINMILE=188582.78    -   If AGE=14 and FINMILE is blank then set FINMILE=194064.37    -   If AGE=15 and FINMILE is blank then set FINMILE=200533.11    -   If AGE=16 and FINMILE is blank then set FINMILE=208003.40    -   If AGE=17 and FINMILE is blank then set FINMILE=213229.71    -   If AGE=18 and FINMILE is blank then set FINMILE=212545.77    -   If AGE=19 and FINMILE is blank then set FINMILE=222148.95    -   If AGE>=20 and FINMILE is blank then set FINMILE=221612.97    -   Compute FINMILE=FINMILE*−0.000001

Point Assignment to TOTOWN1:

-   -   Compute TOTOWN1=TOTOWN1*−0.3849

Recode and Point Assignment to HASSURED:

-   -   If HASSURED equals ‘Y’ then set HASSURED=0.2198    -   Else set HASSURED equal to −0.2198

Create and Point Assignment to POS1:

-   -   Count LEASE=LEASE01 to LEASE10 (‘Y’)    -   Count EMISSION=ECHEK01 to ECHEK30 (‘3030’)

Recode LEASE EMISSION (1 Thru hi=1)

-   -   If LEASE or EMISSION equals 1 then set then set POS1=0.2261    -   Else set POS1=−0.2261

Create and Point Assignment to ACCIDENT

-   -   Count ACCIDENT=ECHEK01 to ECHEK30 (‘3000’)    -   Recode ACCIDENT (1 thru hi=1)    -   If ACCIDENT=1 then set ACCIDENT=−0.2545    -   Else set ACCIDENT=0.2545

Recode and Point Assignment to MAKETXT:

-   -   If MAKETXT=‘Lamborghini’, ‘Saturn’, ‘Ferrari’, ‘Lotus’, ‘Rolls        Royce’, ‘Land Rover’, ‘Porsche’, ‘Suzuki’, ‘Mercedes-Benz’,        ‘Alfa Romeo’, ‘Avanti’, ‘Bentley’, ‘Triumph’, ‘TVR’,        ‘Mitsubishi’, ‘BMW’, ‘Mini’, ‘Jaguar’, ‘Aston Martin’        -   then set MAKETXT=0.4430    -   If MAKETXT=‘DeLorean’, ‘Jeep’, ‘Fiat’, ‘GMC’, ‘Bertone’,        ‘Toyota’, ‘Scion’, ‘Lexus’, ‘Acura’, ‘Hyundai’, ‘Volvo’        -   then set MAKETXT=0.2180    -   If MAKETXT=‘Maserati’, ‘Isuzu’, ‘Mazda’        -   then set MAKETXT=0.0573    -   If MAKETXT=‘Chevrolet’, ‘Ford’, ‘Nissan’, ‘Honda’        -   then set MAKETXT=−0.0464    -   If MAKETXT=‘Volkswagen’, ‘Sterling’, ‘Merkur’, ‘Cadillac’,        ‘Dodge’, ‘Dodge Freightliner’, ‘Freightliner’,        -   then set MAKETXT=−0.1173    -   If MAKETXT=‘Lincoln’, ‘Peugeot’, ‘Oldsmobile’, ‘Buick’, ‘Saab’,        ‘Subaru’, ‘Chrysler’        -   then set MAKETXT=−0.2106    -   If MAKETXT=‘Mercury’, ‘Audi’, ‘Pontiac’, ‘Eagle’, ‘Plymouth’,        ‘AMC’, ‘Renault’, ‘Geo’, ‘GMC Canada’, ‘Lancia’, ‘Daihatsu’,        ‘Yugo’        -   then set MAKETXT=−0.3440    -   If MAKETXT is blank then set MAKETXT=0.0573

Recode and Point Assignment to VEHCLASS:

-   -   If VEHCLASS=‘Upscale—Ultra’, ‘SUV—Premium Large’, ‘CUV—Premium’,        ‘Sport Car—Upper Premium’, ‘Upscale—Premium’, ‘Sport Car—Ultra        Luxury’, ‘Pickup—Full sized’, ‘SUV—Lower Mid Range’, ‘CUV—Mid        Range’, ‘Sport Car—Premium’, ‘Pickup—Small’, ‘SUV—Large’,        ‘SUV—Entry Level’, CUV—Entry Level’        -   then set VEHCLASS=0.4218    -   If VEHCLASS=‘Van—Full Sized’, ‘Upscale—Luxury’        -   then set VEHCLASS=0.2526    -   If VEHCLASS=‘Sport Car—Touring’, ‘Small Car—Budget’,        ‘Upscale—Near Luxury’, ‘Mid Range Car—Premium’        -   then set VEHCLASS=−0.0788    -   If VEHCLASS=‘Mid Range Car—Standard’, ‘Traditional Car’,        ‘Van—Mini’, ‘Small Car—Economy’        -   then set VEHCLASS=−0.2137    -   If VEHCLASS=‘Mid Range Car—Lower’, ‘Sport Wagon—Entry Level’        -   then set VEHCLASS=−0.3819    -   If VEHCLASS=blank then set VEHCLASS=−0.0788

Creation of SCORE for Vehicles 13+Years Old:

-   -   SCORE=(0.0171+MSRPRAT+FINMILE+TOTOWN1+HASSURED+POS1+ACCIDENT+MAKETXT+VEHCLASS)

As shown in the above representative embodiment, the estimated/actualmileage and other factors receive a weighting based on the statisticalanalysis of a sample set. In the detailed example described here, forthe first age class, the number of owners is factored at a weight of−0.0894. If a vehicle is AutoCheck assured, then it gets a 0.4319 factorvalue; alternatively it gets a −0.4319 factor value. If there has beenany record of government or police use, or an accident or theft reportedfor the vehicle, another factor (NEGA1) is given a value of −0.4216 and,if not, a value of 0.4216. Next, different makes of cars are assignedvarious factor values (MAKETXT). Typically high-end and well-made carsobtain greater values, while more budget cars may receive lesser values.A similar determination of the vehicle class is also a factor(VEHCLASS). To generate a score, the previously determined factors arethen summed with 1.7137. This score will be a number between 0.0 and1.00, inclusive. This may represent a probability of the vehicle beingon the road in five more years. In an embodiment, the score reported tothe end user may be multiplied by 100 to give a more recognizable score,such as a percentage. A similar process is used with the other ageclasses, although the weightings are different as can be seen.

Sample Uses

Although providing a vehicle score according to the present disclosureis useful in and of itself, there are a number of additional uses thatmay be helpful to users, whether they be vehicle owners, insurancecompanies, dealerships, and the like. First, it may be helpful toprovide an embodiment to plot a vehicle's score over time. For example,data from the first year of the vehicle's life may be used to find ascore as of a year from its original sale. Similarly data from the firstand second years could be used to determine a score at the end of thesecond year, and so on. This data may be plotted on a chart or graph toshow the decline in score over time. If restoration or repair work isdone and factored into the score, such a score may also increase.Graphing multiple scores may also show the effect of various owners on avehicle's score, if the timing of ownership is plotted as well.

Additionally, in one embodiment, it is possible to project a score intothe future, such as by estimating mileage and likely events in futuremonths and/or assuming similar trending in usage and event history as ina previous time frame. This can help show a vehicle owner when his orher car should best be replaced, the length of time it is likely tolast, or other useful information. In another embodiment, such scorepredicting considers different possible usage and event scenarios (e.g.,conservative, moderate, and heavy usage) to predict a range of futurescoring. As predictions go farther into the future, the possible scoreestimations are likely to expand into a general cone shape from thepresent calculated score. This can help show users best-, worst-, andlikely-case scenarios.

Furthermore, in one embodiment, a score may be used as a factor inproviding individualized valuations for used cars. This can beparticularly useful to user car dealers or interactive websites, such asKelley Blue Book online (http://www.kbb.com).

Alternatives

One embodiment of the system and method described herein provides ascore generator system that generates an automated vehicle specificvaluation of a used car based on the physical and historical attributesof that vehicle. This score may indicate the likelihood that the vehiclewill be on the road in a specific period of time. The score may give anabsolute percentage of such likelihood or it may give a value relativeto all other used vehicles in a database, all other used vehicles of thesame make/model/year, or a certain subset of the vehicles in a database.In one embodiment, the score generator system includes a data linkmodule for linking vehicle data and filter module for applying amulti-level filters that process the linked vehicle data.

Although the foregoing has been described in terms of certain preferredembodiments, other embodiments will be apparent to those of ordinaryskill in the art from the disclosure herein. For example, a vehiclescore may indicate the likelihood of a vehicle being on the road foranother X number of months or years. Although X was discussed as fiveyears in an example above, it would be obvious to vary this betweenthree and eight years or any other period desired. Similarly, scoringmay be based on a car holding up for another Y miles, where Y may be,for example, 36,000 miles, 50,000 miles, or 100,000 miles. The scoringdiscussed above has also been referred to as numerical, but a scorecould be configured as, for example, a set of stars or a grade, such asthe A to F scale typical on elementary school report cards; pluses andminuses may be included to provide more precise grading as well.Additional elements may include actual wholesale or retail prices, orthe actual “popularity” of the vehicle make/model/year combination.Different markets that are served or might be served may get differentrepresentations of the same information, or have the informationpresented in different ways.

The present systems and methods may also be accessed by any of a numberof means, such as through the Internet as already explained or computerto computer transfer, through interactive television services,stand-alone or networked kiosks, automated telephone services and thelike. Scores may be generated or retrieved individually or in batch invarious embodiments. Although much of this disclosure discussesindividual user access, it is understood that lenders, dealers,auctioneers, and others involved or interested in vehicles, particularlythe used vehicle market, may also utilize this system. Moreover, thedescribed embodiments have been presented by way of example only, andare not intended to limit the scope of the disclosure. Indeed, the novelsystems and methods described herein may be embodied in a variety ofother forms without departing from the spirit thereof. Accordingly,other combinations, omissions, substitutions, and modifications will beapparent to the skilled artisan in view of the disclosure herein. Thus,the present disclosure is not limited by the preferred embodiments, butis defined by reference to the appended claims. The accompanying claimsand their equivalents are intended to cover forms or modifications aswould fall within the scope and spirit of the disclosure.

1. A vehicle scoring system, comprising: a computer system having a processor that supports operation of a software application, wherein the computer system is configured to receive a request from a user, the request identifying a specific vehicle with only a vehicle identification number, and to communicate the vehicle identification number to a filter module; a data storage module, accessed on the computer system, including a plurality of vehicle data records obtained from at least one of a title information database, a registration information database, a Department of Motor Vehicle (DMV) records database, an auction records database, and an accident records database; the filter module, executed on the computer system, comprising: a first filter for obtaining relevant vehicle-related data from the data storage module's plurality of vehicle data records using only the vehicle identification number received in the request, the vehicle-related data comprising a plurality of factors; a second filter for comparing the obtained vehicle-related data of a specific vehicle to the obtained vehicle-related data of comparison vehicles and assigning a numerical value to at least one of the factors comprising the relevant vehicle-related data; and a third filter for weighting and combining the numerical values of the relevant vehicle-related data into a vehicle score for the specific vehicle, wherein weighting is determined by a modeling technique applied to the plurality of data records, wherein combining comprises adding the weighted numerical values; and an output module for reporting an indication of the vehicle score to a user, wherein the indication of the vehicle score is one of a number and a range of numbers from a preselected scale, wherein the preselected scale is at least one of 0 to 100, 0 to 10 and 0% to 100%.
 2. The vehicle scoring system of claim 1, wherein the output module comprises a web server.
 3. The vehicle scoring system of claim 1, wherein the data storage module comprises a plurality of databases.
 4. The vehicle scoring system of claim 1, wherein the indication of the vehicle score is a number from a preselected range.
 5. The vehicle scoring system of claim 1, wherein the indication of the vehicle score is a grade.
 6. The vehicle scoring system of claim 3, further comprising: a data link module for linking data from the plurality of databases that is associated with at least the vehicle identifier; and wherein the processor is capable of operating the data link module.
 7. The vehicle scoring system of claim 1, further comprising: a modeling module for providing the filter module with at least one of the first filter, the second filter and the third filter.
 8. The vehicle scoring system of claim 7, wherein the modeling module uses at least a portion of the plurality of vehicle records in the data storage module to determine what types of data are relevant to the vehicle score.
 9. The vehicle scoring system of claim 7, wherein the modeling module uses at least a portion of the plurality of vehicle records in the data storage module to determine relative values of different types of data relevant to the vehicle score. 